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Background: With increasing gaps between the rich and poor, potential risk factors for class conflict have attracted increasing attention from researchers. Although cognitive factors are known to be significant predictors of classconflict behavior, limited attention has been paid to competence stereotypes of the upper class. When considering economic inequality, people pay more attention to competence stereotypes of the upper class, which may have adverse effects. This study aimed to investigate the relationship between competence stereotypes held by the lower class about the upper class and class conflict, and to test the mediating role of intergroup envy in this relationship and the moderating role of upward social mobility belief. Methods: Data were collected from a convenience sample from a comprehensive university in China. Based on scores on subjective and objective class scales, 284 lower-class college students (103 males and 181 females) aged 18-24 were selected to participate (both their subjective and objective scores were lower than 3 points). Their endorsement of upper-class competence stereotypes, intergroup envy, upward social mobility beliefs, and class conflict were measured using a well-validated self-report questionnaire. Results: The main data were analyzed using correlation analysis, the SPSS macro PROCESS (Model 7), and simple slope analysis. The results show a significant positive correlation between competence stereotypes held by lower-class college students toward the higher class and class conflict, and this connection was mediated by intergroup envy. Moreover, the indirect effect of intergroup envy on this link was moderated by upward social mobility beliefs; this effect was stronger for college students with lower upward social mobility beliefs. Conclusion: This study broadens our understanding of how and when competence stereotypes among the lower class concerning the upper class are related to class conflict. Researchers and policymakers should pay special attention to competence stereotypes of the upper class, especially intergroup envy and class conflict among lower-class individuals with lower levels of upward social mobility beliefs. [ABSTRACT FROM AUTHOR]

Urban land use provides essential information about how land is utilized within cities, which is critical for land planning, urban renewal, and early warnings for natural disasters. Although existing studies have utilized multi-source perception data to acquire land use information quickly and at low cost, and some have integrated urban morphological indicators to aid in land use identification, there is still a lack of systematic discussion in the literature regarding the potential of three-dimensional urban morphology to enhance identification effectiveness. Therefore, this paper aims to explore how urban three-dimensional morphology can be used to improve the identification of urban land use types. This study presents an innovative approach called the UMH–LUC model to enhance the accuracy of urban land use identification. The model first conducts a preliminary classification using points of interest (POI) data. It then improves the results with a dynamic reclassification based on floor area ratio (FAR) measurements and a variance reclassification using area and perimeter metrics. These methodologies leverage key urban morphological features to distinguish land use types more precisely. The model was validated in the Pearl River Delta urban agglomeration using random sampling, comparative analysis and case studies. Results demonstrate that the UMH–LUC model achieved an identification accuracy of 81.7% and a Kappa coefficient of 77.6%, representing an 11.9% improvement over a non-morphology-based approach. Moreover, the overall disagreement for UMH–LUC is 0.183, a reduction of 0.099 compared to LUC without urban morphology and 0.19 compared to EULUC-China. The model performed particularly well in identifying residential land, mixed-use areas and marginal lands. This confirms urban morphology's value in supporting low-cost, efficient land use mapping with applications for sustainable planning and management. [ABSTRACT FROM AUTHOR]

During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service status of dams. Therefore, an accurate deformation prediction is an important part of dam safety monitoring. However, due to multiple factors, dam deformation data often tend to be highly volatile, and most existing deformation estimation techniques employ a single algorithm, which may not effectively capture the potential change process. A hybrid model for dam deformation prediction has been proposed to overcome this problem. First, dam deformation data are decomposed into three components by seasonal and trend decomposition using loess. Second, a convolutional neural network–gated recurrent unit (GRU) hybrid model, which optimizes hyperparameters using the sparrow search algorithm, is used to capture the nonlinear relationships that exist in each component. Finally, the final prediction result of dam deformation is the comprehensive output of multiple submodules. The deformation monitoring data (period: 2009–2019) of a parabolic variable-thickness double-curved arch dam located in China are considered as the survey target. The test results indicate that the proposed model is suitable for short-term and long-term prediction and outperforms other models in terms of higher robustness to abnormal sequences than other conventional models (R² differs by 5.50% and 7.87%, respectively, in short-term and long-term predictions for different measurement points, while other models differ by 9.78% to reach 15.71%, respectively). Among the models studied, the GRU shows better robustness to abnormal series than the LSTM with good prediction accuracy, fewer parameters, and a simpler structure. Hence, the GRU can be employed for dam deformation prediction in practical engineering. [ABSTRACT FROM AUTHOR]

Deformations in dam structures can have a critical impact on dam safety and life. Accurate methods for dam deformation prediction and safety evaluation are thus highly needed. Dam deformations can be predicted based on many factors. The analysis of these influences on the deformation of the dam reveals a problem that deserves further attention: dam deformation lags behind environmental factors of the water level and temperature as well as the time lag of the temporal dam deformation data. In this paper, a hybrid deep learning model is proposed to enhance the accuracy of dam deformation forecasting based on lag indices of these factors. In particular, dam deformations are predicted using deep networks based on gated recurrent units (GRUs), which can effectively capture the temporal characteristics of dam deformation. In addition, an improved particle swarm optimization (IPSO) algorithm is used for optimizing the GRU hyperparameters. Furthermore, the complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and the partial autocorrelation function (PACF) are exploited to select the lag factor indices. The accuracy and effectiveness of the proposed CEEMDAN–PACF–IPSO–GRU hybrid model were evaluated and compared with those of other existing models in terms of four different evaluation indices (MAE, MSE, R2 , and RMSE) and using 9-year historical data for the case of a pulp-masonry arch dam in China. The experimental results show that our model outperforms other models in terms of the deformation prediction accuracy (R2 increased by 0.16%–9.74%, while the other indices increased by 14.55% to reach 96.69%), and hence represents a promising framework for general analysis of dam deformations and other types of structural behavior. [ABSTRACT FROM AUTHOR]

Logistics is the booster of economic development, and it is imperative to further improve the logistics energy efficiency and adjust its development model. This paper is an attempt to investigate the logistics energy efficiency and main influencing factors of the Yangtze River Economic Belt, which is the most economically intensive region that spans East, Central, and West China. The input-oriented SBM-DEA model is employed to identify factors such as energy input, undesirable output, and service capacity output as well as the logistics energy efficiency of Yangtze River Economic Belt. Energy efficiency is then further decomposed into pure technical efficiency, scale efficiency, and technical efficiency, from the perspective of which provinces and cities are compared. The research results show that the logistics energy efficiency of Yangtze River Economic Belt needs to be further improved and that energy efficiency differs greatly among cities and provinces, indicating that the development is quite unbalanced in different areas. Therefore, the local government should develop development strategies according to the main influencing factors and constraints to the local logistics industry, so as to optimize and upgrade the energy structure of the logistics industry. [ABSTRACT FROM AUTHOR]

Since the Internet is currently being utilized in initial public offerings (IPOs), there has been a fundamental change in how IPOs can be distributed. Through the Internet, firms issue shares to a wider range of smaller investors. From the perspective of online marketing, we focus on the influences of information disclosure on IPO performance and corporate governance. We analyze if a higher degree of IPO firm performance in the IPO aftermarket is related to corporate governance. We build a corporate governance index CG for IPO firms, composed of equity structure, board characteristics, management style and disclosure quality. Through empirical evidence, we confirm that the threshold of information disclosure has positively effect on IPO firms performance. In particular, IPO firms with higher CG experience significant increases in EPS and ROA in the post-marketing. The impact of corporate governance on IPO firm performance is significant in the economic boom and non-significant in the economic recession. [ABSTRACT FROM AUTHOR]

Since the 24-hr PM 2.5 (particle aerodynamic diameter less than 2.5 μm) concentration standard was regulated in Taiwan in 2012, the PM 2.5 concentration has been decreasing year by year, but the ozone (O 3 ) concentration remains almost the same. In particular, the daily maximum 8-hr average O 3 (MDA8 O 3 ) concentration frequently exceeds the standard. The goal of this study is to find a solution for reducing PM 2.5 and O 3 simultaneously by numerical modeling. After the Volatile Organic Compounds (VOC S )-limited and nitrogen oxides (NO X )-limited areas were defined in Taiwan, then, in total, 50 scenarios are simulated in this study. In terms of the average in Taiwan, the effect of VOC S emission reduction is better than that of NO X on the decrease in PM 2.5 concentration, when the same reduction proportion (20%, 40%) is implemented. While the effect of further NO X emission reduction (60%) will exceed that of VOC S . The decrease in PM 2.5 is proportional to the reduction in precursor emissions such as NO X , VOC S , sulfur dioxides (SO 2 ), and ammonia (NH 3 ). The lower reduction of NO X emission for whole Taiwan caused O 3 increases on average but higher reduction can ease the increase, which suggests the implement of NO X emission reductions must be cautious. When comparing administrative jurisdictions in terms of grids, districts/towns, and cities/counties, it was found that controlling NO X and VOC S at a finer spatial resolution of control units did not benefit the decrease in PM 2.5 but did benefit the decrease in O 3 . The enhanced O 3 control strategies obviously cause a higher decrease of O 3 throughout Taiwan due to NO X and VOC S emission changes when they are implemented in the right places. Finally, three sets of short-term and long-term goals of controlling PM 2.5 and O 3 simultaneously are drawn from the comprehensive rankings for all simulated scenarios, depending on whether PM 2.5 or O 3 control is more urgent. In principle, the short-term scenarios could be ordinary or enhanced version of O 3 decrease with lower NO X /VOC S emissions, while the long-term scenario is enhanced version of O 3 decrease plus high emission reductions for all precursors., (Copyright © 2022 Elsevier Ltd. All rights reserved.)